docs: Add website documentation for using S3 Tables with Spark Operator

analytics/terraform/spark-k8s-operator/README.md

@@ -80,7 +80,7 @@ Checkout the [documentation website](https://awslabs.github.io/data-on-eks/docs/
 | <a name="input_enable_vpc_endpoints"></a> [enable\_vpc\_endpoints](#input\_enable\_vpc\_endpoints) | Enable VPC Endpoints | `bool` | `false` | no |
 | <a name="input_enable_yunikorn"></a> [enable\_yunikorn](#input\_enable\_yunikorn) | Enable Apache YuniKorn Scheduler | `bool` | `false` | no |
 | <a name="input_kms_key_admin_roles"></a> [kms\_key\_admin\_roles](#input\_kms\_key\_admin\_roles) | list of role ARNs to add to the KMS policy | `list(string)` | `[]` | no |
-| <a name="input_name"></a> [name](#input\_name) | Name of the VPC and EKS Cluster | `string` | `"spark-eks-s3tables"` | no |
+| <a name="input_name"></a> [name](#input\_name) | Name of the VPC and EKS Cluster | `string` | `"spark-operator-doeks"` | no |
 | <a name="input_private_subnets"></a> [private\_subnets](#input\_private\_subnets) | Private Subnets CIDRs. 254 IPs per Subnet/AZ for Private NAT + NLB + Airflow + EC2 Jumphost etc. | `list(string)` | <pre>[<br>  "10.1.1.0/24",<br>  "10.1.2.0/24"<br>]</pre> | no |
 | <a name="input_public_subnets"></a> [public\_subnets](#input\_public\_subnets) | Public Subnets CIDRs. 62 IPs per Subnet/AZ | `list(string)` | <pre>[<br>  "10.1.0.0/26",<br>  "10.1.0.64/26"<br>]</pre> | no |
 | <a name="input_region"></a> [region](#input\_region) | Region | `string` | `"us-west-2"` | no |

website/docs/benchmarks/spark-operator-benchmark/graviton-r-data.md

@@ -53,7 +53,7 @@ In the table below we have taken the Median times from the output for each insta
 
 To calculate the performance increase we are calculating a ratio of the query times. For example, to determine how much faster the r8g instances were compared to the r6g instances:
 - Find the times corresponding to each query, using `q20-v2.4` as an example the r6g.12xlarge took `2.81s` and the r8g.12xlarge took `1.69s`.
-- We then divide r5g.12xlarge/r8g.12xlarge, for q20-v2.4 thats `2.81s/1.69s = 1.66`. So for this query the r8g.12xlarge was able to complete the queries 1.66 times faster (or a ~66% percent improvement)
+- We then divide r5g.12xlarge/r8g.12xlarge, for q20-v2.4 that's `2.81s/1.69s = 1.66`. So for this query the r8g.12xlarge was able to complete the queries 1.66 times faster (or a ~66% percent improvement)
 
 The data has been sorted by the last column, showing the performance increase r8g.12xlarge has over the r6g.12xlarge.
 <div class="benchmark-results">

website/docs/benchmarks/spark-operator-benchmark/running-the-benchmark.md

@@ -146,7 +146,7 @@ When you enter the results directory you will see a list of folders which corres
 
 ![S3 bucket showing timestamp directories for results](./img/results-s3-timestamps.png)
 
-You can find the latest result by selecting the timestamp thats largest, or find the folder that corresponds to the time of your test.
+You can find the latest result by selecting the timestamp that's largest, or find the folder that corresponds to the time of your test.
 Inside this folder you will see a file with a name like `part-00000-000000000-0000-0000-0000-000000000-0000.json`, this file includes the full spark configuration used for the job.
 ![S3 bucket showing results files](./img/results-s3-result-folder.png)
 

website/docs/bestpractices/preload-container-images.md

@@ -84,7 +84,7 @@ spec:
       ebs:
         volumeSize: 150Gi
         volumeType: gp3
-        kmsKeyID: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab" # Specify KMS ID if you use custom KMS key
+        kmsKeyID: "arn:aws:kms:<REGION>:<ACCOUNT_ID>:key/1234abcd-12ab-34cd-56ef-1234567890ab" # Specify KMS ID if you use custom KMS key
         snapshotID: snap-0123456789 # Specify your snapshot ID here
 ```
 

website/docs/blueprints/data-analytics/datahub-on-eks.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 6
 sidebar_label: DataHub on EKS
 ---
 # DataHub on EKS

website/docs/blueprints/data-analytics/observability-spark-on-eks.md

@@ -1,6 +1,6 @@
 ---
-sidebar_position: 3
-sidebar_label: Observability Spark on EKS
+sidebar_position: 5
+sidebar_label: Spark Observability on EKS
 ---
 
 import TaxiTripExec from './_taxi_trip_exec.md';

website/docs/blueprints/data-analytics/spark-eks-ipv6.md

@@ -1,6 +1,6 @@
 ---
-title: Spark Operator running on Amazon EKS IPv6
-sidebar_position: 6
+title: Spark Operator on EKS with IPv6
+sidebar_position: 3
 ---
 
 This example showcases the usage of Spark Operator running on Amazon EKS in IPv6 mode. the idea is to show and demonstarte running spark workloads on EKS IPv6 cluster.

website/docs/blueprints/data-analytics/superset-on-eks.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 5
+sidebar_position: 7
 sidebar_label: Superset on EKS
 ---
 # Superset on EKS